Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions

Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions

We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of e...

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Bibliographic Details
Main Authors: Andreas B. Martinsen, Anastasios M. Lekkas, Sébastien Gros, Jon Arne Glomsrud, Tom Arne Pedersen
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-03-01
Series:Frontiers in Robotics and AI
Subjects:
reinforcement learning
trajectory tracking
optimal control
model-based adaptive control
approximate dynamic programming (ADP)
dynamic positioning (DP)
Online Access:https://www.frontiersin.org/article/10.3389/frobt.2020.00032/full

Internet

https://www.frontiersin.org/article/10.3389/frobt.2020.00032/full

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